Unplanned turbine downtime is expensive twice over: lost generation and rushed repairs. The fix is better-timed maintenance. We looked at forecasting failures early enough to schedule around them.
When a wind turbine fails without warning, the owner pays twice. The turbine sits idle and generates nothing, and the repair becomes an emergency job at emergency prices. The usual defenses are running equipment until it breaks, or servicing everything on a fixed calendar, which means paying to maintain parts that were fine.
This study looked at a third option: predict failures early enough to schedule the repair on your own terms.
Here's how the six CRISP-DM steps played out on this one.
Maintenance is a scheduling problem before it's anything else. A repair needs technicians, parts, and sometimes a crane, and all of them are cheaper and easier to arrange with notice. So a useful prediction isn't just "this part will fail." It has to arrive early enough for a planner to actually book the work. That requirement, warning a planner can use, became the goal the whole project was measured against.
Turbines record their own operating history, and that history holds the early signs of trouble. The work here was learning which signals relate to the health of which components, then preparing the data so a model could see a failure building instead of just noise.
We framed it as forecasting: estimate which components are trending toward failure, and roughly when. We'll spare you the technical details. What matters is that the output was a warning with a time window attached, which is what a planner needs, rather than an alarm that fires when it's already too late to plan anything.
Two failure modes would make a system like this useless. Warnings that come too late can't change the schedule, and warnings that are wrong too often teach people to ignore the system. So "good" meant early enough to act on and reliable enough to trust, both judged from the planner's chair, not from a model report.
The endpoint was connecting the forecast to the maintenance schedule itself: use the predicted failure window to decide what gets serviced and when. The crew is booked, the parts are ordered, and the failure that was coming never happens.
This is the pattern for our industrial work: carry the prediction all the way to the decision it's supposed to improve. For a client, we'd build this around your equipment and how your maintenance is planned today.
This was an applied study. We're happy to talk through how a predictive maintenance pilot could be structured.
Let's look at whether your data can give you lead time.